Financial markets run on edge. Artificial intelligence promises an informational advantage, but the reality is plagued by overfitting, survivorship bias, and marketing hype. We analyze what actually works.
The barrier to entry for machine learning has dropped to zero. Anyone can pipe historical price data into an LSTM neural network and generate a chart that perfectly predicts the past. This is overfitting, and it destroys capital.
Real AI applications in investing are narrower, harder, and less glamorous. They involve Natural Language Processing (NLP) to parse subtle sentiment shifts in 10-K filings, clustering algorithms for statistical arbitrage, and hidden Markov models for regime detection.
Run 10,000 simulations using geometric Brownian motion to visualize the probability distribution of future portfolio values.
Determine optimal position sizing based on your edge and odds to maximize long-term compound growth without risking ruin.
Calculate theoretical call/put prices using Black-Scholes and extract implied volatility to find mispriced derivatives.
Evaluate risk-adjusted returns and visualize the impact of adding uncorrelated assets to your portfolio.
Estimate the maximum expected loss over a specific time horizon at a given confidence interval.
Calculate maximum historical drawdowns and recovery times to stress-test your systematic strategies.
Analyze rolling correlations to ensure your diversified portfolio isn't actually a single systemic bet.
Normalize position sizes across assets based on Average True Range (ATR) to equalize risk contribution.
Measure an asset's volatility relative to the broader market to calculate cost of equity and CAPM.
Basic NLP dictionary approach (Loughran-McDonald) to score the polarity of corporate text excerpts.
Calculate intrinsic value through discounted cash flows, stress-testing terminal growth rates.
Published: Oct 2023
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